Esempio n. 1
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 def test_normalize(self):
     img = np.arange(0, 9).reshape(3, 3)
     value_range_in = (0, 10)
     value_range_out = (0, 1)
     expected = np.array([[0, .1, .2],
                          [.3, .4, .5],
                          [.6, .7, .8]])
     calculated = ImageTools.normalize(img,
                                       value_range_in=value_range_in,
                                       value_range_out=value_range_out)
     np.testing.assert_array_almost_equal(expected, calculated)
Esempio n. 2
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 def test_normalize_0to255(self):
     img = np.arange(-4, 5).reshape(3, 3) / 5
     value_range_in = (-1, 1)
     value_range_out = (0, 255)
     expected = np.array([[25.5, 51.,  76.5],
                          [102., 127.5, 153.],
                          [178.5, 204., 229.5]])
     calculated = ImageTools.normalize(img,
                                       value_range_in=value_range_in,
                                       value_range_out=value_range_out)
     np.testing.assert_array_almost_equal(expected, calculated)
Esempio n. 3
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 def test_normalize_clip(self):
     img = np.arange(0, 9).reshape(3, 3)
     value_range_in = (0, 5)
     value_range_out = (0, 1)
     expected = np.array([[0, .2, .4],
                          [.6, .8, 1],
                          [1, 1, 1]])
     calculated = ImageTools.normalize(img,
                                       value_range_in=value_range_in,
                                       value_range_out=value_range_out,
                                       clip=True)
     np.testing.assert_array_almost_equal(expected, calculated)
Esempio n. 4
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def get_ndvi(red, nir, vrange=(-1, 1), dtype=np.float32):
    """
    Calculate the NDVI (Normalized-Difference Vegetation Index)

    :param red: The red band dataset
    :type red: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
    :param nir: The nir band dataset
    :type nir: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
    :param vrange: The range of output values as tuple. By default: (-1, 1).
    :type vrange: tuple of int
    :param dtype: The output dtype.
    :type dtype: :class`np.dtype`
    :return: The NDVI as numpy array.
    :rtype: :class:`Common.GDalDatasetWrapper.GDalDatasetWrapper`
    """

    if nir.extent != red.extent or nir.epsg != red.epsg:
        raise ValueError("Cannot calculate NDSI on two different extents.")

    if nir.resolution != red.resolution:
        # Resize to nir resolution in this case.
        tr = " ".join([str(i) for i in nir.resolution])
        ds_red = ImageTools.gdal_translate(red, tr=tr, r="cubic")
    else:
        ds_red = red

    # TODO Add new test

    img_red = np.array(ds_red.array, dtype=np.float32)
    img_nir = np.array(nir.array, dtype=np.float32)

    # Compensate for nan:
    np.seterr(divide='ignore', invalid='ignore')
    img_ndvi = np.where((img_red + img_nir) != 0, (img_red - img_nir) / (img_red + img_nir), -1)

    # Scale to vrange
    img_ndvi_scaled = ImageTools.normalize(img_ndvi, value_range_out=vrange, value_range_in=(-1, 1),
                                           dtype=dtype, clip=True)

    return GDalDatasetWrapper(ds=nir.get_ds(), array=img_ndvi_scaled)